Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations893
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.9 KiB
Average record size in memory88.1 B

Variable types

Numeric14
Categorical2

Alerts

Ram is highly overall correlated with priceHigh correlation
price is highly overall correlated with Ram and 2 other fieldsHigh correlation
resolution_height is highly overall correlated with price and 1 other fieldsHigh correlation
resolution_width is highly overall correlated with price and 1 other fieldsHigh correlation
ROM_type is highly imbalanced (83.9%)Imbalance
warranty is highly imbalanced (75.6%)Imbalance
name is uniformly distributedUniform
ROM has 12 (1.3%) zerosZeros

Reproduction

Analysis started2024-08-06 04:39:52.169068
Analysis finished2024-08-06 04:40:56.597089
Duration1 minute and 4.43 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

brand
Real number (ℝ)

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0526316
Minimum0
Maximum29
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:25:56.990728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q314
95-th percentile21
Maximum29
Range29
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.9619991
Coefficient of variation (CV)0.65859292
Kurtosis0.16440031
Mean9.0526316
Median Absolute Deviation (MAD)5
Skewness0.64260638
Sum8084
Variance35.545433
MonotonicityNot monotonic
2024-08-06T10:25:57.366245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9 186
20.8%
14 169
18.9%
3 157
17.6%
6 107
12.0%
1 84
9.4%
15 65
 
7.3%
21 28
 
3.1%
2 16
 
1.8%
12 15
 
1.7%
13 9
 
1.0%
Other values (20) 57
 
6.4%
ValueCountFrequency (%)
0 2
 
0.2%
1 84
9.4%
2 16
 
1.8%
3 157
17.6%
4 1
 
0.1%
5 3
 
0.3%
6 107
12.0%
7 6
 
0.7%
8 8
 
0.9%
9 186
20.8%
ValueCountFrequency (%)
29 1
 
0.1%
28 4
 
0.4%
27 8
 
0.9%
26 3
 
0.3%
25 1
 
0.1%
24 1
 
0.1%
23 4
 
0.4%
22 3
 
0.3%
21 28
3.1%
20 3
 
0.3%

name
Real number (ℝ)

UNIFORM 

Distinct815
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.85442
Minimum0
Maximum814
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:25:57.784918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.6
Q1204
median401
Q3609
95-th percentile771.4
Maximum814
Range814
Interquartile range (IQR)405

Descriptive statistics

Standard deviation234.97754
Coefficient of variation (CV)0.57897002
Kurtosis-1.1828226
Mean405.85442
Median Absolute Deviation (MAD)202
Skewness0.014281278
Sum362428
Variance55214.445
MonotonicityNot monotonic
2024-08-06T10:25:58.209161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 4
 
0.4%
297 4
 
0.4%
194 3
 
0.3%
390 3
 
0.3%
762 3
 
0.3%
24 2
 
0.2%
380 2
 
0.2%
766 2
 
0.2%
439 2
 
0.2%
209 2
 
0.2%
Other values (805) 866
97.0%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 2
0.2%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
814 1
0.1%
813 1
0.1%
812 1
0.1%
811 2
0.2%
810 1
0.1%
809 1
0.1%
808 1
0.1%
807 1
0.1%
806 1
0.1%
805 1
0.1%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct464
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79907.41
Minimum9999
Maximum450039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-08-06T10:25:58.662742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9999
5-th percentile26995.4
Q144500
median61990
Q390990
95-th percentile199990
Maximum450039
Range440040
Interquartile range (IQR)46490

Descriptive statistics

Standard deviation60880.044
Coefficient of variation (CV)0.76188233
Kurtosis9.5323276
Mean79907.41
Median Absolute Deviation (MAD)22390
Skewness2.7096943
Sum71357317
Variance3.7063797 × 109
MonotonicityNot monotonic
2024-08-06T10:25:59.066217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49990 16
 
1.8%
37990 13
 
1.5%
59990 12
 
1.3%
47990 11
 
1.2%
64990 11
 
1.2%
54990 11
 
1.2%
79990 11
 
1.2%
33990 11
 
1.2%
57990 11
 
1.2%
62990 11
 
1.2%
Other values (454) 775
86.8%
ValueCountFrequency (%)
9999 1
 
0.1%
10990 3
0.3%
12990 1
 
0.1%
13990 1
 
0.1%
14490 1
 
0.1%
14990 1
 
0.1%
15990 2
0.2%
16990 1
 
0.1%
17990 1
 
0.1%
18990 2
0.2%
ValueCountFrequency (%)
450039 1
0.1%
429990 1
0.1%
420000 1
0.1%
419990 1
0.1%
415000 1
0.1%
399999 1
0.1%
390914 1
0.1%
362999 1
0.1%
344990 1
0.1%
339990 1
0.1%

spec_rating
Real number (ℝ)

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.379026
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-08-06T10:25:59.426886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q166
median69.323529
Q371
95-th percentile80
Maximum89
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5415547
Coefficient of variation (CV)0.07987363
Kurtosis1.3926635
Mean69.379026
Median Absolute Deviation (MAD)2.3235294
Skewness0.8621133
Sum61955.471
Variance30.708828
MonotonicityNot monotonic
2024-08-06T10:25:59.798170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
69.32352941 292
32.7%
60 48
 
5.4%
71 44
 
4.9%
70 43
 
4.8%
62 42
 
4.7%
64 39
 
4.4%
67 39
 
4.4%
66 37
 
4.1%
65 37
 
4.1%
69 35
 
3.9%
Other values (20) 237
26.5%
ValueCountFrequency (%)
60 48
5.4%
61 7
 
0.8%
62 42
4.7%
63 32
3.6%
64 39
4.4%
65 37
4.1%
66 37
4.1%
67 39
4.4%
68 6
 
0.7%
69 35
3.9%
ValueCountFrequency (%)
89 5
0.6%
88 4
 
0.4%
86 4
 
0.4%
85 6
0.7%
84 4
 
0.4%
83 9
1.0%
82 6
0.7%
81 3
 
0.3%
80 12
1.3%
79 10
1.1%

processor
Real number (ℝ)

Distinct184
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.400896
Minimum0
Maximum183
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:00.207080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q137
median62
Q3112
95-th percentile163
Maximum183
Range183
Interquartile range (IQR)75

Descriptive statistics

Standard deviation45.795809
Coefficient of variation (CV)0.61552766
Kurtosis-0.7547947
Mean74.400896
Median Absolute Deviation (MAD)30
Skewness0.57525463
Sum66440
Variance2097.2561
MonotonicityNot monotonic
2024-08-06T10:26:00.630163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 49
 
5.5%
58 41
 
4.6%
34 38
 
4.3%
15 36
 
4.0%
40 33
 
3.7%
68 18
 
2.0%
126 17
 
1.9%
42 17
 
1.9%
129 17
 
1.9%
73 16
 
1.8%
Other values (174) 611
68.4%
ValueCountFrequency (%)
0 1
 
0.1%
1 4
0.4%
2 1
 
0.1%
3 1
 
0.1%
4 2
0.2%
5 2
0.2%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
183 1
 
0.1%
182 1
 
0.1%
181 1
 
0.1%
180 1
 
0.1%
179 1
 
0.1%
178 1
 
0.1%
177 4
0.4%
176 9
1.0%
175 2
 
0.2%
174 2
 
0.2%

CPU
Real number (ℝ)

Distinct29
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.470325
Minimum0
Maximum28
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:00.999377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median18
Q324
95-th percentile28
Maximum28
Range28
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.2608235
Coefficient of variation (CV)0.59861856
Kurtosis-1.2576884
Mean15.470325
Median Absolute Deviation (MAD)8
Skewness-0.29267463
Sum13815
Variance85.762852
MonotonicityNot monotonic
2024-08-06T10:26:01.344200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
28 130
14.6%
19 126
14.1%
1 125
14.0%
24 102
11.4%
6 83
9.3%
17 55
6.2%
8 50
 
5.6%
18 44
 
4.9%
22 43
 
4.8%
16 36
 
4.0%
Other values (19) 99
11.1%
ValueCountFrequency (%)
0 2
 
0.2%
1 125
14.0%
2 26
 
2.9%
3 2
 
0.2%
4 2
 
0.2%
5 2
 
0.2%
6 83
9.3%
7 9
 
1.0%
8 50
 
5.6%
9 1
 
0.1%
ValueCountFrequency (%)
28 130
14.6%
27 4
 
0.4%
26 2
 
0.2%
25 4
 
0.4%
24 102
11.4%
23 1
 
0.1%
22 43
 
4.8%
21 5
 
0.6%
20 3
 
0.3%
19 126
14.1%

Ram
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2418813
Minimum0
Maximum6
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:01.641556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4018289
Coefficient of variation (CV)0.74087503
Kurtosis-1.8864431
Mean3.2418813
Median Absolute Deviation (MAD)0
Skewness0.20754731
Sum2895
Variance5.7687821
MonotonicityNot monotonic
2024-08-06T10:26:01.966863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 456
51.1%
6 369
41.3%
3 40
 
4.5%
4 22
 
2.5%
5 3
 
0.3%
0 2
 
0.2%
2 1
 
0.1%
ValueCountFrequency (%)
0 2
 
0.2%
1 456
51.1%
2 1
 
0.1%
3 40
 
4.5%
4 22
 
2.5%
5 3
 
0.3%
6 369
41.3%
ValueCountFrequency (%)
6 369
41.3%
5 3
 
0.3%
4 22
 
2.5%
3 40
 
4.5%
2 1
 
0.1%
1 456
51.1%
0 2
 
0.2%

Ram_type
Real number (ℝ)

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7402016
Minimum0
Maximum11
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:02.279523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median2
Q34
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3731794
Coefficient of variation (CV)0.63450576
Kurtosis-0.24288961
Mean3.7402016
Median Absolute Deviation (MAD)0
Skewness1.0620087
Sum3340
Variance5.6319807
MonotonicityNot monotonic
2024-08-06T10:26:02.641404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 499
55.9%
4 166
 
18.6%
8 145
 
16.2%
6 41
 
4.6%
5 14
 
1.6%
7 13
 
1.5%
11 7
 
0.8%
1 3
 
0.3%
9 2
 
0.2%
3 1
 
0.1%
Other values (2) 2
 
0.2%
ValueCountFrequency (%)
0 1
 
0.1%
1 3
 
0.3%
2 499
55.9%
3 1
 
0.1%
4 166
 
18.6%
5 14
 
1.6%
6 41
 
4.6%
7 13
 
1.5%
8 145
 
16.2%
9 2
 
0.2%
ValueCountFrequency (%)
11 7
 
0.8%
10 1
 
0.1%
9 2
 
0.2%
8 145
 
16.2%
7 13
 
1.5%
6 41
 
4.6%
5 14
 
1.6%
4 166
 
18.6%
3 1
 
0.1%
2 499
55.9%

ROM
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9305711
Minimum0
Maximum6
Zeros12
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:02.932588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q35
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7435645
Coefficient of variation (CV)0.44359063
Kurtosis-0.78223859
Mean3.9305711
Median Absolute Deviation (MAD)0
Skewness-1.0494897
Sum3510
Variance3.0400173
MonotonicityNot monotonic
2024-08-06T10:26:03.238276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 634
71.0%
1 188
 
21.1%
2 42
 
4.7%
0 12
 
1.3%
3 10
 
1.1%
6 5
 
0.6%
4 2
 
0.2%
ValueCountFrequency (%)
0 12
 
1.3%
1 188
 
21.1%
2 42
 
4.7%
3 10
 
1.1%
4 2
 
0.2%
5 634
71.0%
6 5
 
0.6%
ValueCountFrequency (%)
6 5
 
0.6%
5 634
71.0%
4 2
 
0.2%
3 10
 
1.1%
2 42
 
4.7%
1 188
 
21.1%
0 12
 
1.3%

ROM_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.7 KiB
1
872 
0
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters893
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

Length

2024-08-06T10:26:03.567546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-06T10:26:03.908046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 872
97.6%
0 21
 
2.4%

GPU
Real number (ℝ)

Distinct134
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.7514
Minimum0
Maximum133
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:04.251701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.6
Q153
median80
Q3115
95-th percentile126
Maximum133
Range133
Interquartile range (IQR)62

Descriptive statistics

Standard deviation36.399977
Coefficient of variation (CV)0.44525203
Kurtosis-1.1236854
Mean81.7514
Median Absolute Deviation (MAD)32
Skewness-0.36958627
Sum73004
Variance1324.9583
MonotonicityNot monotonic
2024-08-06T10:26:04.690729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 107
 
12.0%
126 63
 
7.1%
112 61
 
6.8%
79 55
 
6.2%
80 54
 
6.0%
111 51
 
5.7%
33 50
 
5.6%
63 42
 
4.7%
31 39
 
4.4%
53 36
 
4.0%
Other values (124) 335
37.5%
ValueCountFrequency (%)
0 1
 
0.1%
1 3
0.3%
2 1
 
0.1%
3 4
0.4%
4 2
 
0.2%
5 3
0.3%
6 1
 
0.1%
7 5
0.6%
8 1
 
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
133 1
 
0.1%
132 2
 
0.2%
131 1
 
0.1%
130 1
 
0.1%
129 1
 
0.1%
128 2
 
0.2%
127 4
 
0.4%
126 63
7.1%
125 3
 
0.3%
124 4
 
0.4%

display_size
Real number (ℝ)

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.173751
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-08-06T10:26:05.044874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.93909503
Coefficient of variation (CV)0.061889444
Kurtosis0.56612155
Mean15.173751
Median Absolute Deviation (MAD)0
Skewness-0.80858315
Sum13550.16
Variance0.88189948
MonotonicityNot monotonic
2024-08-06T10:26:05.373895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
15.6 464
52.0%
14 214
24.0%
16 112
 
12.5%
13.3 28
 
3.1%
16.1 21
 
2.4%
17.3 14
 
1.6%
14.1 7
 
0.8%
11.6 7
 
0.8%
17 5
 
0.6%
13.4 4
 
0.4%
Other values (8) 17
 
1.9%
ValueCountFrequency (%)
11.6 7
 
0.8%
13.3 28
 
3.1%
13.4 4
 
0.4%
13.5 2
 
0.2%
13.6 2
 
0.2%
14 214
24.0%
14.1 7
 
0.8%
14.2 4
 
0.4%
15 3
 
0.3%
15.3 2
 
0.2%
ValueCountFrequency (%)
18 1
 
0.1%
17.3 14
 
1.6%
17 5
 
0.6%
16.2 2
 
0.2%
16.1 21
 
2.4%
16 112
 
12.5%
15.6 464
52.0%
15.56 1
 
0.1%
15.3 2
 
0.2%
15 3
 
0.3%

resolution_width
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035.3931
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-08-06T10:26:05.768415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation426.07601
Coefficient of variation (CV)0.20933353
Kurtosis4.9916138
Mean2035.3931
Median Absolute Deviation (MAD)0
Skewness1.8360547
Sum1817606
Variance181540.77
MonotonicityNot monotonic
2024-08-06T10:26:06.091087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1920 680
76.1%
2560 70
 
7.8%
1366 41
 
4.6%
2880 40
 
4.5%
3840 14
 
1.6%
3200 11
 
1.2%
1080 8
 
0.9%
1600 5
 
0.6%
3024 4
 
0.4%
3456 4
 
0.4%
Other values (8) 16
 
1.8%
ValueCountFrequency (%)
1080 8
 
0.9%
1200 4
 
0.4%
1280 2
 
0.2%
1366 41
 
4.6%
1440 1
 
0.1%
1600 5
 
0.6%
1920 680
76.1%
2160 3
 
0.3%
2240 2
 
0.2%
2256 1
 
0.1%
ValueCountFrequency (%)
3840 14
 
1.6%
3456 4
 
0.4%
3200 11
 
1.2%
3072 1
 
0.1%
3024 4
 
0.4%
2880 40
4.5%
2560 70
7.8%
2496 2
 
0.2%
2256 1
 
0.1%
2240 2
 
0.2%

resolution_height
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1218.3247
Minimum768
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-08-06T10:26:06.419453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum3456
Range2688
Interquartile range (IQR)120

Descriptive statistics

Standard deviation326.75688
Coefficient of variation (CV)0.26820179
Kurtosis5.7277028
Mean1218.3247
Median Absolute Deviation (MAD)0
Skewness2.1707962
Sum1087964
Variance106770.06
MonotonicityNot monotonic
2024-08-06T10:26:06.812648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1080 577
64.6%
1200 101
 
11.3%
1600 53
 
5.9%
768 41
 
4.6%
1800 34
 
3.8%
1440 15
 
1.7%
1920 13
 
1.5%
2400 11
 
1.2%
2000 9
 
1.0%
2560 6
 
0.7%
Other values (12) 33
 
3.7%
ValueCountFrequency (%)
768 41
 
4.6%
1024 2
 
0.2%
1080 577
64.6%
1200 101
 
11.3%
1280 1
 
0.1%
1400 2
 
0.2%
1440 15
 
1.7%
1504 1
 
0.1%
1600 53
 
5.9%
1620 6
 
0.7%
ValueCountFrequency (%)
3456 1
 
0.1%
2560 6
 
0.7%
2400 11
 
1.2%
2234 2
 
0.2%
2160 6
 
0.7%
2000 9
 
1.0%
1964 4
 
0.4%
1920 13
 
1.5%
1864 2
 
0.2%
1800 34
3.8%

OS
Real number (ℝ)

Distinct14
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.447928
Minimum0
Maximum13
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2024-08-06T10:26:07.129407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q112
median12
Q312
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9839119
Coefficient of variation (CV)0.17329877
Kurtosis15.808599
Mean11.447928
Median Absolute Deviation (MAD)0
Skewness-4.015505
Sum10223
Variance3.9359066
MonotonicityNot monotonic
2024-08-06T10:26:07.474742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
12 782
87.6%
10 28
 
3.1%
3 18
 
2.0%
11 15
 
1.7%
7 12
 
1.3%
9 10
 
1.1%
1 10
 
1.1%
13 9
 
1.0%
8 2
 
0.2%
2 2
 
0.2%
Other values (4) 5
 
0.6%
ValueCountFrequency (%)
0 1
 
0.1%
1 10
1.1%
2 2
 
0.2%
3 18
2.0%
4 2
 
0.2%
5 1
 
0.1%
6 1
 
0.1%
7 12
1.3%
8 2
 
0.2%
9 10
1.1%
ValueCountFrequency (%)
13 9
 
1.0%
12 782
87.6%
11 15
 
1.7%
10 28
 
3.1%
9 10
 
1.1%
8 2
 
0.2%
7 12
 
1.3%
6 1
 
0.1%
5 1
 
0.1%
4 2
 
0.2%

warranty
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size50.7 KiB
1
819 
2
 
59
3
 
9
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters893
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Length

2024-08-06T10:26:07.845744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-06T10:26:08.248970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 893
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 819
91.7%
2 59
 
6.6%
3 9
 
1.0%
0 6
 
0.7%

Interactions

2024-08-06T10:25:51.005345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:53.512400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:57.872279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:02.564523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:06.803621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:11.159956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:15.617397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:19.640282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:24.407378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:29.303339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:33.663112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:38.054706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:42.750465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:46.891031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:51.297341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:53.841928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:58.180609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:03.029302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:07.171167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:11.465080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:15.908950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:19.933767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:24.769100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:29.587016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:33.953512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:38.456649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:43.060199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:47.184575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:51.614913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:54.151597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:58.493262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:03.329857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:07.532838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:12.029022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:16.213213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:20.257302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:25.093533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:29.909815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:34.288564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:38.776717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:43.373168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:47.515743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:51.886629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:54.440193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:58.780370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:03.614856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:07.830062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:12.310420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:16.490410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:20.531424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:25.450189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:30.177853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:34.555188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:39.066671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:43.636662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:47.784121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:52.183423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:54.742455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:59.080452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:03.890910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:08.199920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:12.616305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:16.776400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:20.829167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:25.820267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:30.486892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:34.868562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:39.369916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:43.956347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:48.083878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:52.488398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:55.048655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:59.446833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:04.195809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:08.501666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:12.919284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:17.078414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:21.125435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:26.221819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:30.856866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:35.172209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:39.683238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:44.250947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:48.386657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:52.774814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:55.489832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:59.770171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:04.450904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:08.795103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:13.202942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:17.348475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:21.455994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:26.558990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:31.210794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:35.454762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:39.992965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:44.548727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:48.666043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:53.054365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:55.786712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:00.096675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:04.696117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:09.084137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:13.509438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:17.640647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:21.802960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:26.948819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:31.537777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:35.762889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:40.313822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:44.841871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:48.963459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:53.340236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:56.069272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:00.464708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:04.954669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:09.376798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:13.797008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:17.915692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:22.159822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:27.303130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:31.885917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:36.051927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:40.649056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:45.145123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:49.253329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:53.640219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:56.377416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:00.865731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:05.213197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:09.676674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:14.104597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:18.206375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:22.780364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:27.663761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:32.209701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:36.347813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:41.022696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:45.421258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:49.541736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:54.348533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:56.676217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:01.246267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:05.519298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:09.985766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:14.396925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:18.484427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:23.121078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:28.051203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:32.493913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:36.646298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:41.345227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:45.739080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:49.829812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:54.653666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:56.977535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:01.653760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:05.870001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:10.287256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:14.703607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:18.780451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:23.440737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:28.403707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:32.791027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:37.165470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:41.674451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:46.032042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:50.132940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:54.944624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:57.285197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:01.963748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:06.129084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:10.582443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:14.996121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:19.071425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:23.754059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:28.733765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:33.073178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:37.441957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:42.031131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:46.301306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:50.430886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:55.218917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:24:57.581213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:02.268829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:06.447860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:10.853470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:15.302738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:19.348149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:24.110484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:29.006979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:33.369244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:37.743382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:42.451344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:46.591347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-08-06T10:25:50.715431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-08-06T10:26:08.516194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CPUGPUOSROMROM_typeRamRam_typebranddisplay_sizenamepriceprocessorresolution_heightresolution_widthspec_ratingwarranty
CPU1.000-0.259-0.1280.1340.1090.122-0.1310.036-0.0230.011-0.2780.182-0.156-0.107-0.0570.087
GPU-0.2591.000-0.0240.0410.0000.1790.0220.073-0.378-0.143-0.329-0.300-0.084-0.185-0.2790.131
OS-0.128-0.0241.0000.0610.382-0.1520.084-0.0020.1220.0490.046-0.047-0.004-0.0100.0450.302
ROM0.1340.0410.0611.0000.4400.153-0.1860.025-0.149-0.032-0.332-0.137-0.285-0.288-0.2940.040
ROM_type0.1090.0000.3820.4401.0000.3730.1760.1160.3190.1030.0890.2530.1580.1120.0000.000
Ram0.1220.179-0.1520.1530.3731.000-0.222-0.010-0.081-0.164-0.570-0.124-0.342-0.256-0.2030.061
Ram_type-0.1310.0220.084-0.1860.176-0.2221.0000.051-0.041-0.1360.3510.2560.3790.2570.1880.104
brand0.0360.073-0.0020.0250.116-0.0100.0511.0000.015-0.1730.020-0.106-0.003-0.0340.0060.366
display_size-0.023-0.3780.122-0.1490.319-0.081-0.0410.0151.0000.0340.2680.0320.1130.1750.3470.082
name0.011-0.1430.049-0.0320.103-0.164-0.136-0.1730.0341.0000.1590.0440.1950.1990.0480.145
price-0.278-0.3290.046-0.3320.089-0.5700.3510.0200.2680.1591.0000.1510.6130.5150.4320.102
processor0.182-0.300-0.047-0.1370.253-0.1240.256-0.1060.0320.0440.1511.0000.1080.0790.1750.122
resolution_height-0.156-0.084-0.004-0.2850.158-0.3420.379-0.0030.1130.1950.6130.1081.0000.7020.2760.082
resolution_width-0.107-0.185-0.010-0.2880.112-0.2560.257-0.0340.1750.1990.5150.0790.7021.0000.2530.070
spec_rating-0.057-0.2790.045-0.2940.000-0.2030.1880.0060.3470.0480.4320.1750.2760.2531.0000.089
warranty0.0870.1310.3020.0400.0000.0610.1040.3660.0820.1450.1020.1220.0820.0700.0891.000

Missing values

2024-08-06T10:25:55.670988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-06T10:25:56.340918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
096474990073.0000001051962511915.61920.01080.0121
19383990060.0000003418625112615.61920.01080.0121
214402699069.3235291517625112314.01920.01080.0121
3147845972966.000000396185111114.02240.01400.0121
423786999069.3235291602162219113.32560.01600.071
511593999062.000000396625112314.01920.01080.0121
662993679060.0000003418625112615.61920.01080.0121
714117699063.000000612214515315.61920.01080.0121
836884899064.000000426625112115.61920.01080.0121
9211987499068.000000396185112313.31080.01920.0121
brandnamepricespec_ratingprocessorCPURamRam_typeROMROM_typeGPUdisplay_sizeresolution_widthresolution_heightOSwarranty
883616911999073.00000071714515315.61920.01080.0121
884681218749079.000000771414116316.02560.01600.0121
885616512569975.00000071714515315.61920.01080.0121
88611054999069.3235295618165111214.01920.01080.0131
8871925699069.323529581185111115.61920.01080.0121
88836974499069.323529801862519515.61920.01080.0121
889352811000071.0000001222410115215.62560.01440.0111
890350618999089.0000001432434116314.02560.01600.0121
891353412999073.00000073812515315.61920.01080.0121
892353013199084.0000001432412115315.61920.01080.0121